Quantification of Carbon Emission Technologies Based on Knowledge Graph Bert-BiLSTM-Attention-CRF Model

With global climate change becoming increasingly serious, reducing carbon emissions has become a topic of common concern worldwide. As an important component of green transportation, electric vehicles have enormous potential for carbon reduction. However, developing countries often face problems such as backward technology and insufficient infrastructure, which limit their ability to adopt electric vehicle technology. The purpose of this paper is to propose a strategy to quantify electric vehicle technologies and exchange them for carbon credits to facilitate technology transfer and carbon emission reduction. This paper proposes a strategy for quantifying electric vehicle technologies and exchanging them for carbon credits: in quantifying the value of electric vehicle technologies, the quantitative value of manufacturing processes, software, and other intellectual property rights is collected and calculated by using intelligent algorithms, such as NLP, to quantify the potential lifespan, efficiency, and performance of these technologies. Using patent information selection as the basis for judging technology value, we adopt the method of constructing a knowledge graph and build a Bert-BiLSTM-Attention-CRF model that incorporates the attention mechanism, thus accurately and effectively quantifying the technical value, potential life and many other aspects of electric vehicle technologies. In the quantification of carbon credits, it is first necessary to establish a measurement baseline by selecting three aspects: the current carbon emissions of the calculation country, the current market value of carbon credits, and the potential future value of carbon credits. Based on these quantitative results, the CRITIC weighting method is calculated and harmonized into a total quantitative result. Finally, some reliable and effective suggestions for international carbon credit trading are made based on the model evaluation results.